CS691/692 IT691 Capstone Project, Seidenberg Department of CSIS, Pace University, NY

Quantum Security for Classical ComputersEvelyn Krivorotova, Patrick Leggett, Wildenslo Osias, Avery Leider and Charles C. Tappert

Seidenberg School of Computer Science and Information Systems, Pace UniversityPleasantville, NY 10570, USA

Email: {ek64045n, pl94947n, wo39632n, aleider, ctappert}@pace.edu

Abstract—The power of quantum computing created by qubitsand processes such as superpositioning and entanglement havethe potential to not only break certain forms of cryptographyand machine learning, but it will also have the ability to makefar-reaching gains on digital forensics, especially in regard toacquiring digital evidence. Quantum computers will not replaceclassical computers - instead, using a quantum computer willbe like using a graphics card - in a system of computing,certain tasks will be sent to the quantum computer for moreefficient processing. What security tasks will best be selectedfor quantum processing? What packaging of the data or thequery needs to take place in order for the quantum computerto do its job? In answering some of these questions, we havechosen to conduct hand-on exercises using IBM Q Experience(which also has the Jupyter powered Qiskit), and the FalconSandbox Public API (used with tools such as Network Miner) inorder to see how close quantum computing has come to realizingsome of these hypothetical capabilities. Our research has shownthat quantum computing and its use of parallel actual actionsusing qubits will make it possible to make advances in forensictactics, especially when using fault-tolerant quantum machines,though new frameworks unique to security purposes may haveto be written. In near-term quantum computing, there is the realpotential to be able to preserve more evidence in an inst.ant.

Index Terms—quantum computing, Grover’s algorithm, IBMQ Experience, quantum circuits, Qiskit, Qiskit Aqua, QASMlanguage, forensic analysis tool, open API, hybrid programs,Network Miner, Falcon Sandbox.

I. INTRODUCTION

Quantum computing combines classical computer sciencewith quantum mechanics. Researchers, in their commitment tosolving problems, are studying the area of quantum computingto find a faster and more efficient way to solve problems.Quantum computing differs in many ways from classicalcomputing, the most significant difference being the wayinformation is processed. In classical computing, informationis stored in bits, which takes the value of one of the two binarydigits 0 or 1. On the other hand, using the law of quantummechanics, quantum computing stores information in qubits.A qubit is a quantum system that encodes the two classical bits(0,1) into two identifiable states while allowing for in-betweenstates using quantum phenomena such as superposition andentanglement.

In classical computing, a bit can only have one state at atime, either 1 or 0. In quantum computing, on the other hand,things do not have to follow this clear-cut dichotomy. Thingscan exist in states that might look mutually exclusive to us.For instance, while in classical computing a certain particle

Thanks to the IBM Faculty Award to Dr Tappert that supports this research.

must have either 1 or 0 as value, in quantum computing aparticle can simultaneously have the value of both 1 and 0.This is called superposition. Simply put, superposition aims atfreeing us from binary constraints [17].

Entanglement refers to the strong relationship that existsamong quantum particles. Entangled particles can learn abouteach other’s state independently of their distance from eachother. For example, if two qubits are entangled, and one ofthe qubits is measured, it automatically tells you the resultyou will receive when you measure the other one, regardlessof the distance between the two. This strong correlation isdescribed by Albert Einstein as spooky action at a distance[9].

Forensics has made incredible gains over the last fewdecades in regards to the rapid advances in cloud computingtechnology. Quantum computing stands to upend this entireparadigm by allowing governments and other organizationsto break existing cryptographic measures, which will causedigital forensics specialists to rethink how best to find valuableinformation. Certainly, it will be possible that certain issuessuch as factoring and discrete logs, where the hardness ofsecurity is ensured, (ex. RSA, DSA, ECDSA) will have theircryptography standards broken by algorithms such as Shor’s[18] due to the advent of fault-tolerant quantum systemswith millions of error-corrected qubits [14]. Grover’s searchalgorithm, which features in unstructured search, could bepotentially used to search the key spaces of a system encryptedby AES [16]. The potential of quantum computers is such thata lot of these cryptosystems would be able to be infiltrated inmere minutes compared to potentially thousands of years withclassical computing systems. These encryption schemes areused every day in cases such as online certificates, encryptedemail, and Virtual Private Network (VPN) connections [14].

Nevertheless, it again should be noted that we are a longway away from having a quantum computer that is advancedenough (and with enough qubits) to be able to execute Shor’salgorithm. For instance, there will be post-quantum encryptionschemes that are made (and being made right now) that donot use factoring or any type of discrete log math problems togenerate encryption/decryption keys. These algorithms wouldnot be at risk of decryption on a quantum computer like RSAor ECC encryption would, and are vital in ensuring a safetransition to the quantum era without having to contend withunsavory black hat hackers who would easily be able to breakinto government installations and businesses alike. Thoughseveral of these algorithms currently exist in some form, they

still need to be put through the paces before we can executethem on a satisfactory scale [14].

We should also consider some of the more positive aspectsof how security can be enhanced such as the advent ofquantum computing in a classical setting, with one of the bestexamples talked about today being quantum key distribution,or QKD. QKD involves classical authenticated channels anduntrusted quantum channels alike establishing a shared secretkey between two spatially separated parties. This distributionof information theoretic secure key expansion would not beexecutable in the context of strictly classical concepts. Theimportance here is placed on a protocol with informationtheoretic security, meaning that security is assured even withthe addition of quantum computing, as the security is not basedon any particular assumption [19]. We have seen interest inthe advent of quantum gadgets and quantum fingerprinting (setup when end users share the same bit stream using minimalcommunication). Another note worth mentioning is the trait ofdecoherence in quantum computing, which essentially meansthat you can only do so many operations in a row beforesome information is lost, which could cause trouble for moretraditional breaches such as man-in-the-middle attacks.

Again, the question that we are posing in this paper is: howcan we package our data in a way that we can further some ofthe advances being developed now and in the coming decades?We feel that some of the biggest advances yet to come in thisfield is through digital forensics, as this topic has yet to bediscussed at length through discussions on quantum computingconcepts.

In order to answer this question, we have organized ourpaper in the particular format: in Section II we took a lookinto previous research regarding cybersecurity measures usingquantum methods. Section III describes some of the softwareand other tools used to execute our experiments. SectionIV ventures into our processes in regard to using IBM QExperience and Falcon Sandbox to explore new possibilities,while Section V describes the preliminary findings that derivedfrom that process. Section VI discusses future work into thefield that we deem necessary in this paper, while Section VIIconcludes our paper and provides final words to our readers.

II. LITERATURE REVIEW

A. Quantum Computing

This paper introduces basic concepts of quantum computingand touches upon recent developments in the fast growingquantum computing field. The elementary unit of quantuminformation is the quantum bit. It can be viewed as a two-state system (e.g. two-level atom). The power of quantumcomputing is based on the ability of quantum systems to be inthe superposition of its basic states. All of the states are multi-plied simultaneously, therefore there is an enormous quantumparallelism. In order to perform quantum computations, thefollowing conditions have to be met: (i) a two-level system(u0. and u1.) as a qubit, (ii) the ability to prepare the qubitin a given state, say u0., (iii) the capability of measuringeach qubit, (iv) construction of basic gate operations such

as conditional logic gate (the control-not gate), and (v) asufficiently long decoherence time [13].

Not only is quantum computing a field that still has avast potential for discovery and growth, actually buildinga quantum computer is a task that has only very recentlygotten traction with companies such as Google and IBM,who are in a race to build the first fault tolerant quantumcomputer. This more advanced system would create a quantumcomputer with a sufficient enough amount of error correctionacting alongside a low amount of noise. Only recently, Googlehas made the claim of being the first company to achievequantum supremacy by solving a problem that would be closeto impossible to solve on a classical system.

Figure 1. An image of bit and qubit

B. Cybersecurity in an era with quantum computers: will webe ready?

Studying some of the solutions currently being discussed inregard to applying security through quantum computing, oneof the burgeoning fields has been post-quantum cryptography.This is based on conventional ciphers that have origins inmathematics other than factoring, which also may have moreof a chance against attack from unsavory characters armedwith quantum computing technology [15].

With these rapidly changing fields come the increasinglylikelihood of more quantum gadgets that find practical use,with the uses eventually reaching more broad audiences. Forinstance, there has been noted potential in improving efficiencyby making breakthroughs with fewer resources through quan-tum fingerprinting [15].

C. Global catastrophic risk and security implications of quan-tum computers

There has also been some new research done on othercryptography algorithms that stand as a guard against otherquantum computers. Secret-key based cryptography has alot of potential, though the amount of computational power

achieved over a classical based computer needs to be consid-ered. Non-repudiation such as digital signatures are also notcurrently a possibility, keeping secret-key algorithms a fairlyniche solution at the moment [14].

Code-based cryptography, which is based on matrix mul-tiplications and vectors, has the ability to run in a quickand efficient manner. However, the size of the public keyskeeps this form of cryptography from having been utilized,even though the system is said to be highly efficient at keygeneration, encryption, and decryption [8].

There have been some questions as to why we shouldcurrently worry about the threat of quantum computers to ourcurrent security structures. In a way, this is planning for afuture that may happen in 10, 20, 30, 40 or even 50 yearsfrom now. But the idea here is to improve the efficiency ofour post-quantum cryptography methods, build a consensus ofconfidence among the community, and improve the usabilityby methods such building out software and hardware [8].

D. Cryptographic Distinguishability Measures for QuantumMechanical States

Quantum cryptography is built around the idea that “physi-cal information carriers are always quantum-mechanical” [10].This concept creates enormous possibilities within cryptog-raphy. In all quantum cryptographic problems, informationis enclosed into a quantum-mechanical state. These states,more precisely the distance between them, determine theease of information accessibility and retrieval [10]. Fourmeasures of distinguishability (distance) are discussed: theprobability of identification error, the Kolmogorov distance,the Bhattacharyya coefficient, and the Shannon distinguisha-bility. Various inequalities arise between the four measureswhen the quantum notion of distinguishability is based uponclassical measures. These inequalities can be used in goodprotocol design, which require the “probability of cheating” forevery participant to vanish exponentially. In terms of quantumapplication, two families of quantum states are considered. Thederived inequalities show that the four notions are equivalent,and “it is legitimate to speak for a single, unified exponentialindistinguishability for two families of quantum states [10].”

E. Grover’s Algortihm

There have already been cases of implementing Grover’salgorithm in cloud quantum systems such as IBM Q Experi-ence. The Grover’s algorithm itself is well adept at being ableto successfully perform a search on a quantum system thatwould not be quite as satisfactory on a classical computer.For instance, we could take a look at the issue of trying tosearch an unsorted list in a classical algorithm. This is reducedto a scan of N values done in sequence, which would usean average of N/2 steps. However, when using a quantumcomputer, those marked elements would have the square rootof N steps while using Grover’s algorithm [11].

There have been simulations with Grover’s algorithm thatmanages to come up with the answer x = (1,1) every timeit is ran. In this case of an actual quantum system, Coles

and Eidenbenz ran the job 1,024 times using ibmqx4 andx = (1,1) appeared 662 times with (0,0), (0,1), and (1,0)taking place 119, 101, and 142 times respectively, meaning a65/100 success rate in finding the correct answer. In a perfectworld (and perhaps a few decades from now), we would beable to have a quantum system that has the accuracy of thesimulator, though deviation is still likely to take place becauseof substandard error correction in place, or due to the overalldepth of the circuit, as a gate with a larger depth is sure tocause a lower success rate [7].

F. Quantum Computer Search Algorithms; Can we outperformthe classical search algorithms?

In the paper Quantum Computer Search Algorithms, the au-thors discuss the major differences between classical and quan-tum computers. The distance property that allows quantumcomputers to solve problems at a higher speed than classicalcomputers is referred to as superposition. Superposition allowsfor multiple operations to be performed spontaneously, whichresults in the time complexity to go down from O(logN)in the classical computers to O(

√N) in quantum. Grover’s

algorithm is able to perform a search in the shorter time ofO(

√N). This algorithm is using a host of gates and operators.

For instance, Hadamard gates, through which the qubits areput, are used to calculate amplitude for every possible input.The relationship between the probability and the amplitude islinear. The paper also states that the probability for every itemwill be the same. The amplitudes of an input can be flippedthrough utilization of unitary operators based on the oraclefunction f(x). The diffusion operator, when used, amplifies theamplitude of the desired input and changes the probability ofevery input. The paper highlights the difficulty in predictingthe results when using such algorithms because of the manyunknowns in quantum computing. However, it has been shownthat the use of quantum computers, for specific tasks, can besignificantly more efficient [12].

Figure 2. An image of a quantum circuit

III. PROJECT REQUIREMENTS

A. IBM Q Experience

IBM Q experience is a leading quantum cloud servicesand software platform. It provides access to a set of IBM’sprototype quantum processors for research and applicationpurposes. It was launched in May 2016 and as of May 2018,IBM Q Experience has 3 processors: two 5-qubit processorand a 16-qubit processor. Though IBM’s designed graphicuser interface (GUI), Circuit Composer, we will be able toconstruct quantum algorithms and run quantum experiments.As an alternative, we will also use the composer in a scriptingmode and write programs in QASM-language [2].

Figure 3. An image of a Complete Qiskit Workflow

B. Qiskit and Qiskit Aqua

Qiskit is an open-source quantum computing software de-velopment framework which supports Python 3.5 or later. It isrecommended to install Anaconda and use Jupyter Notebook,which is already included in Anaconda, for interacting withQiskit. The workflow of using Qiskit consists of three high-level steps: build, execute, and analyze. The building stepconsists of developing a desired quantum circuit in the com-poser that represents a problem at hand. During the executionstep, the experiment is ran on the system’s backend, aswell as the simulator’s backend. The analysis part consistsof calculating summary statistics and visualizing the results.Figure 3 represents a complete workflow in Qiskit [4].

Qiskit Aqua is a library of quantum algorithms. It is cross-domain and currently allows users to experiment on chemistry,finance, AI, and optimization applications. Fig. 4 represents amodular, multi-level Aqua library. Since it is also extendable,using Python users of different levels can contribute. AddingAqua to Qiskit made it “the only scientific software frameworkfor quantum computing that is capable of taking high-leveldomain-specific problem specifications down to circuit gener-ation, compilation, and finally execution on IBM Q quantumhardware [5].” Unlike other quantum software libraries, Aquadoes not require an API or another intermediate layer for thehybrid programs. Instead it has an ability to directly interfacewith the classical software.

Figure 4. Representation of an Aqua library that is modular and extensibleat multiple levels

C. Falcon Sandbox Public API

Falcon Sandbox is an open and free API that can be usedto submit files and URLs for analysis. It can also pull reportdata and conduct advanced searches. Every API key comeswith an authorization level of either restricted, default, elevatedor super. Different endpoints become accessible based on theprivileges. Free accounts, by default, can issue restricted keysto search the database. In order to perform data downloads,it is possible to upgrade to the full default key. The new APIv2 only supports one way of authentication, and it has anexcellent open-source Python wrapper library called VxAPI.It covers all major API endpoints available [1].

D. Network Miner

Network Miner is an open-source forensic analysis tool forWindows. The features of the Network Miner are networkforensics, network sniffing, PCAP parser, digital forensics,and a packet sniffer. It can detect the operating systems, hostnames, and open ports of network hosts. It can also extracttransmitted files from network traffic [3].

IV. METHODOLOGY

In order to begin with our process to research forensics andhow it can work with quantum computing, we first installedQiskit by installing Anaconda (which houses Jupyter alongwith Anaconda Prompt, a CLI for Windows), then activated

our Python virtual environment. In this case, installing Qiskitrequired installing a standard package-management systemnamed Pip, which generally comes preinstalled in the Pythonlanguage which we worked with. After typing in “pip installqiskit”, those packages should install in your virtual environ-ment. In order to use Qiskit in an environment such as this, it isrequired that an API token is copied from IBM Q Experience,which is also used to create an account configuration file. Oncethis is done, Jupyter Notebook can begin through AnacondaPrompt by typing “jupyter notebook”.

However, in the process of installing Qiskit through Ana-conda, we discovered that IBM Q Experience has recentlybeen given the ability for quantum developers to work withQiskit (using Jupyter Notebooks) in the cloud that all takesplace in one environment, which allowed us to build circuits,execute results, and have all code directly in one place. Oncethis was established, we began to build Grover’s algorithmin IBM’s ibmqx4 5-qubit computer. Regarding this paradigm,there has been simulations of Grover’s Algorithm in otherforms such as 3-qubit quantum systems, and the stability ofquantum logic circuit elements’ perturbations was verified.Once these perturbation values are increased, it is sure toshow an upscaled probability that the wrong state is detected.Here, Grover’s algorithm was being built on a circuit composerhousing 5-qubits, which would study how the increase inqubit size has the probability of changing detection rates anddistortion impact [11].

Circuit Composer in IBM Q Experience consists of a seriesof gates, barriers, and operations which are used to build yourcircuits. For instance, you can easily place a Hadamard gate,which is useful for producing superpositions, onto a q line. ACX, or a controlled-NOT gate, can then be placed on a pairof qubits to act as a control and a target. Once the controlbit is in superposition, this would allow for entanglement.From here, you can add measurements to q[0] along withq[1] to build a simple entangled two qubit quantum circuit.Viewing the results of the constructed Grover’s Algorithm withIBM’s ibmqx4 5-qubit computer, while there was still a higherprobability of 00 or 11 in our tests, there was also a high errorrate in which 01 and 10 threatened to become even with thosesame probabilities. As it is possible to run the algorithm asa simulation as well, we conducted this experience with theresult that 01 and 10 actually came to equal or surpass 00 or 11in probabilities, which led us to assume that our interpretationof Grover’s algorithm required more work in order to receivethe desired result.

Figure 5. An image of quantum test result

Using IBM Q Experience, there was also the opportunity touse openQASM in order to transfer the code to other avenuessuch as Qiskit. While there are potential methods of doing sothrough a circuit.importQASM command, we elected to copyand paste this code into Jupyter Notebooks while making theappropriate changes there.

The goal with our implementation of Grover’s algorithmwas to execute the circuit on IBM’s 5-qubit computer withtwo time slots that coincided with state preparation, 13 slotsfor being able to implement a Toffoli gate (also known asa controlled-controlled-not gate), 7 slots for implementing anoperator, and 2 slots for adding measurements. We expectedthat when this algorithm is fully functional, we would be ableto see a success rate that is close to perfect in the simulator.The more complex the qubits, the less satisfactory the resultsmay be, especially with a more complex implementation ofthe Toffoli gate [7].

Figure 6. An image of simulator test result

Tying all of this together was the Falcon Sandbox PublicAPI, which was used in order to attempt to test the speedof files being sent for analysis. This was done by obtainingan API key through scripts labeled on the Hybrid Analysiswebsite, which were then transferred over to Qiskit Aquafor processing. These keys do have authorization levels ofrestricted, default, elevated, and super, though it is possibleto upgrade these keys to full default keys by giving access todata downloads.

When it came time to see how we could better integrateGrover’s algorithm, we settled on using Qiskit Aqua. This isbecause some of the most widely used quantum algorithms,including Grover’s, were already built into the stack. Thisalso allowed us to consider some of the possibilities thatwould arise if open source forensics software (such as FalconSandbox Public API) could be engineered to work with such asystem. Grover’s algorithm has a basis in unstructured search,which combined with IBM Q Experience and Qiskit, couldusher in an era where search queries could be leveragedwith quantum technology to solve problems even faster thanprevious classical systems. And as Falcon Sandbox Public APIhas the ability to search not only file names, but has theability to look for various domains, tags, and IP addresses,the capabilities of these advanced search features have thepotential to view a fair increase in power, even if we canonly currently view this potential on near-term quantum sys-tems. Using the API started with downloading the VxAPIwrappers from the Falcon Sandbox API GitHub page. Thisversion has now since been upgraded to V2, which implies

Figure 7. Grover’s Algorithm in Circuit Composer

improved performance in addition to a more simplified meansof authentication. From here, it involved installing in whateverversion of Python (3.4 or above) that we had on hand, thoughin order for the experiment to take place, we placed this APIinto Qiskit by specifying the API secret key along with theapplication server.

What we ultimately decided on was to attempt to lever-age the Falcon Sandbox API alongside a problem that wassomewhat inspired by the documentation displayed at theQiskit Community page. This is a problem that has hadsome previous success in being executed successfully in QiskitAqua, and it served as a means to search quickly for data in aquantum setting. It refers to a fairly basic Boolean formulawhereas we had the possibility of hitting on variables orinterpretations that satisfied the input called the 3-Satisfiability(or 3-SAT) problem. With Boolean variables, we were tryingto see as to whether the sequences could be laid out in away that can consistently be labeled TRUE or FALSE, and ifthe formula can be deemed satisfiable by being evaluated asTRUE. If the formula was deemed not satisfiable, it would beevaluated as FALSE [6].

The power of quantum computing gave us pause as towhether these problems could be expanded to 4-SAT or even5-SAT, as they would be deemed NP-complete, though inthe case of 5-SAT modifications would have to be made. Inthis case, we have created a Boolean function f that has fiveBoolean variables, v1, v2, v3, v4, v5 stated as:

f (v1, v2, v3, v4, v5) = (¬v1∨ ¬v2∨ ¬v3∨ ¬v4∨ ¬v5)∧ (v1∨v2∨ ¬v3∨ ¬v4∨ ¬v5)∧ (¬v1∨ v2∨ ¬v3∨ v4∨ ¬v5)∧ (v1∨¬v2∨ v3∨ ¬v4∨ v5)∧ (¬v1∨ ¬v2∨ v3∨ v4∨ v5)

This is somewhat different from the Qiskit guide examplein that while the number of clauses is the same, the variableshave been expanded to five, though they are kept simple inorder to preserve its basic nature. The ¬ symbol is used as theBoolean NOT that flips the value from the previous one. Thereare other symbols used such as the ∨ symbol, which is the

Boolean OR, and the ∧ symbol which is the Boolean symbolfor AND. In order to make the f satisfiable here, that wouldrequire v1, v2, v3, v4, v5 to have a set of values that evaluatesto TRUE.

With this foundation in place, we looked to load a fewscripts that were used in these types of problems such asQuantumInstance, which is a type of backend for execution,a Qiskit visualization tool, Grover’s Algorithm itself (whichhas its source code embedded within Qiskit Aqua), and a toolfor parsing DIMACS CNF format strings and building theassociated oracle circuit. We also wrote code as a means to usethe oracle to produce a Grover instance, then ran further codein order to query a backend called qasm simulator. Furthercode is then applied in order to execute this backend whileprinting the results.

V. PRELIMINARY FINDINGS

As noted by the 3-SAT example on the community Qiskitpage, there was a successful attempt at executing a 3-SATproblem using Grover’s algorithm in Aqua and is able to showresults labeled as binary strings, showing three solutions thatare satisfied and which all display high probabilities. In theirparticular example, it is shown that the binary string 000 has a.285 probability, the 011 binary string has a .294 probability,and the 101 binary 101 has a .272 probability, which are allsignificantly higher than the other five. While these proba-bilities are not necessarily as high as other measurements,given the nature of the 3-SAT Boolean problem, they do showenough improvement to show satisfying solutions [6].

A 5-SAT problem, while much more complex, would beNP-complete and should be able to show similar results(though at somewhat lower probabilities) in a simulator back-end. Part of what we are proposing in regards to futurework is new additions to the Qiskit Aqua element that willaccommodate these more complex problems (in conjunctionwith new forensic pluggable components) that could achievefurther breakthroughs in quantum computing.

Figure 8. Qiskit Pseudocode Executing Satisfiability Problem

The ability to test 5-SAT problems, much less 3-SATproblems, on a near-term quantum system may be a significantdistance away, as there are current limits to decoherence timein our current quantum systems that would be no match forthe thousands of gates and length of device strings that wouldhave to be sent over a network. These quantum circuits arecontinuing to be built at IBM and will surely be able tosolve more problems dealing with unstructured search (amongothers) in time.

VI. FUTURE WORK

As previously mentioned, there are limits to decoherencetime in our current quantum computing system. Therefore,there should be a focus on building a system that can performcomputational tasks before decohering. Second, as of now, thelarger the computational task, the more costly it is to simulatea quantum circuit. Therefore, another area of improvementwould be to build a scalable system that could perform bothsmall and large tasks without a considerable increase in thesize and cost of the hardware. Third, some attention shouldbe given to the engineering of an error correction system withlogical qubits (error correction qubits), which would allowprogrammers to solve their problems. That said, even thoughthe fault-tolerant quantum system is still at best decades away,we are nevertheless entering an incredible growth period inquantum computing, and much work is left to be done ina variety of fields, some of which has of yet encountered asignificant amount of research, one of those fields being digitalforensics.

Luckily, tools developed in the last couple years (suchas Qiskit Aqua) provide the opportunity to one day easily

leverage classical computational software such as EnCase orFTK along with a near-term quantum system for anythingfrom full text searching to media analysis. Also, brand newopen source forensics tools could one day be specificallyengineered to interact with cloud quantum systems such asIBM Q Experience. This would have the effect of easing thebarrier to testing new ways to greatly increase the speed ofwhich a hard drive can be searched before that information iseither removed by the perpetrators or compromised by othermeans.

As stated, Aqua currently provides a library of algorithmsand pluggable Python scripts in fields such as optimization, AI,Finance, and Chemistry. These can be used to simulate exper-iments involving tasks such as molecular structures, portfoliodiversification, credit risk analysis, and vehicle routing. Someof these programs are made so that the user would have toimplement very few lines of code in order to effectively runtheir experiments, while some have support baked right intoQiskit Aqua in order to interface more seamlessly with theclassical software. One of these programs is called PySCF,which is used to create simplified workflows using the basisof chemistry.

In this same spirit, we would propose that a new element isadded to Qiskit Aqua that is focused on digital forensics, aswe have to realize that the more advanced quantum systemsbecome in our society, the more power that cyber criminalswill be able to yield in breaking longstanding encryptionschemes while leaving behind minimal traces. We couldleverage classical computational software packages (much likePySCF) to work seamlessly with Qiskit Aqua to produce

new breakthroughs, while also utilizing GUIs to test variousalgorithms at different stages. While there are ways to buildpluggable components and algorithms in Qiskit Aqua now,a network of components would spur innovation in a fieldthat would allow researchers to take advantage of the currentabsence of others to observe quantum states in a quantumcomputer, an advantage that would be successful in gettingthe upper hand on hackers and various cybersecurity threats.Like Qiskit Finance, there could be guides on how quantumalgorithms could be used on methods such as email analysisor keystroke logging.

Figure 9. Qiskit Executing Satisfiability Problem Flowchart

VII. CONCLUSION

Because of the promise to perform computational tasks at anexponentially faster pace than classical computing, quantumcomputing has been on the radar of many researchers whohave been devoting a sizable amount of time and effort toexplore ways to solve problems using this new technology.As quantum researchers, we too have been intrigued by thechallenges as well as the potential of quantum computing, andhow it could be used in the capacity of forensics research. Inthis paper, we have established differences between quantumcomputing and classical computing in addition to explainingthe key terms and processes in quantum computing. We haveutilized IBM Q experience, Qiskit and its Qiskit Aqua element,Grover’s Algorithm, and Falcon Sandbox API to show howdigital forensics has the potential to take the next step withquantum systems. Finally, we have discussed suggestionsabout future work that would better serve the field of digitalforensics.

On a final note, we would like to emphasize that whilethe implications of finding evidence on a quantum system (orusing a quantum system to find evidence) can not be lookedat as an immediate threat to forensic cases and cybersecurityas a whole, we cannot stress enough how important these

paradigms will become in the future, as with every new pieceof technology comes a criminal prepared to exploit it for theirown personal gain. Especially with quantum systems, thereis an opportunity to be able to extract the most hidden dataat a much quicker pace while implementing safeguards thatwill head off any possible avenues towards nefarious activities.There are one of many roads we could go down with this newtechnology. Let us take steps to ensure that we choose thesafest and most secure road possible so that control is neverlost to a new era of information warfare.

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