Solution Benefits

Quantum solution infrastructure typically offers a high degree of automation and delivers significantly improved performance and resilience. A platform powered by a quantum computing framework evolves through continuous learning and deep learning. During the evolution, the system becomes a self-reliant and self-sustained platform. The platform will be able to make critical decisions.

This new paradigm of computing will change the way software is written. The framework evolved from basic programming software to patterns and models. This paradigm will change the deployment and management of the applications. These solutions are based on deep learning–based frameworks, quantum computing platforms, and a new set of tools. These quantum solution frameworks will enable organizations to create, test, and deploy new applications without any human touch points. The development cycle will help in rebuilding the software with deep learning and quantum computing algorithms.

The infrastructure for a quantum solution needs to be highly scalable. The quantum solutions platform requires the high-performance computing power to be able to process data loads and guarantee service assurance and performance. Current applications lack performance and service levels on a millisecond basis and millions of cycles per second. High-quality quantum solutions will be built with quantum algorithms and quantum AI techniques. New quantum solutions infrastructure will use the GPU-level computing power blended with quantum computing. This infrastructure will leverage open source software and the cloud for elastic capacity demand.

Quantum solutions infrastructure will be IoT enabled. Data connectivity and real-time insight are important factors for real-time quantum solutions. This solution infrastructure will have features to secure data transfer, ingestion, and data processing. The data will be acquired from various data sources to create real-time insights. IoT solution deployments will have all connected devices. A policy and governance framework will be part of quantum solutions.

Quantum solution infrastructure needs to be secure. Enterprises will have quantum solutions that involve cybersecurity for the infrastructure. The servers need to be monitored, and the security data needs to be ingested, integrated, and stored for analysis. The security and monitoring events need to be filtered using unsupervised learning. The threat intelligence model is built and has people, places, and applications deployed on the server. Prevention of the threats is possible through an event dashboard.

Automating Manual and Semi-manual Processes

Quantum solutions involve the automation of business processes. Automation helps in enhancing the businesses and enterprises by changing the way they operate. Quantum solutions involve artificial intelligence, machine learning, robotic process automation, and business process automation methods. These show immediate benefits in enterprises. See Figure 1-8.

Handling big data is the biggest automation effort that quantum solutions can provide with their high-performant data processing capabilities. Human errors and problems arise when adding automation to the business processes. Predictive and prescriptive analytics are solutions that can add value to the enterprise business’s decision-making.

The data comes from the different servers, applications, devices, and departments in a business organization. The data needs to be stored, cleaned, and analyzed for actionable decisions. Quantum solutions help in handling big data and creating dashboards and reports.

Reducing Costs and Improving Profits

The effort and cost for deployment and manageability need to be balanced with the quality of the solution. Having fewer resources, minimizing the operational costs, and delivering high-quality solutions are the key goals for enterprise IT. Quantum solutions help cut down the infrastructure costs with exponential speedup in the algorithms compared to the classical algorithms. See Figure 1-9.

Many enterprises have challenges related to resources dedicated to the maintenance of the applications (80 percent of them). The operating costs of the deployed solutions are twice the solution development or package software cost. The developer resource costs are more than the software servers’ cost by a ratio of 18:1. Banks are spending about a billion dollars per year to maintain their applications and solutions that are up and running 24 hours every day of the week. This amounts to the total cost of ownership and challenges for maintaining IT in a bank.

The other challenges are related to legacy apps and products, outdated technology platforms, and not having skilled resources in the IT departments. Time to market is an important factor in every aspect of the business. Technology platforms do not have the capabilities to match the speed expected in terms of time to market. The volume and complexity of change or requirements do not match the resources available in the enterprise.

Having effective solutions is important to handle this exponential increase in the cost of maintaining the deployed solutions. Quantum solutions solve this problem by cutting the resources required by offering an exponential speedup in the algorithms.

The quantum solutions infrastructure will be highly performant, secure, available, and resilient, and it will be able to match the time to market needs. The people needed will require average skills but will be able to deploy these solutions easily and upgrade them for maintenance. Overall, enterprises will be able to handle their customer requirements quickly and develop products faster . See Figure 1-10.

Quantum solution infrastructure will have data models, patterns, and algorithms to process data quickly. First, The data can be from various sources, and processing power helps in creating reports. Decision-making will be faster using the reports and dashboards. The total cost of ownership will go down for an enterprise.

Second, this complexity is a significant limiting factor for executing product development at the speed necessary to respond to market needs. In the absence of careful planning, technology platforms will fail to anticipate the complexity of interactions. Managing volume and complexity falls to a highly skilled human workforce, the demand for which is already far outstripping supply.

Without effective solutions, the cost of maintaining applications will, therefore, increase exponentially. Furthermore, increasing complexity means a greater risk of security loopholes, making organizations more vulnerable to threats.

Improving Efficiencies and Reducing the Defects

The manageability of the solution infrastructure is the key for quantum solutions. There will be an impact to the cost and security of the solution infrastructure because of the complexity in handling and managing the applications. Visibility and monitoring of the applications deployed are achievable with threat intelligence models using deep learning. New deployments, upgrades, and releases can be handled with change and impact analysis. This analysis helps in identifying IT problems and maintaining the deployed applications without issues and errors. Defects cost 100 times more to fix in the production stage compared to the design stage. See Figure 1-11.

Cryptography

Banks will be interested in protecting their payment gateways and solutions against security threats from future quantum computing capabilities. There is a need to make the payment solutions “quantum-resistant.” In addition, there is a need for blockchain-based banking and financial solutions to improve using post-quantum cryptographic algorithms.

The Quantum Resistant Ledger is the next-generation solution for banking and financial services. Quantum cryptography is based on quantum key distribution. Quantum key distribution is related to the expansion of a short, shared key into an infinite shared stream. This demands improvement in the efficiency of post-quantum cryptographic algorithms.

Financial firms need to start investing in post-quantum cryptography. Blockchain-based solutions will be not quantum-resistant if they are not upgraded to post-quantum cryptographic algorithms. Functioning cryptographic systems are DES, Triple DES, AES, RSA, Merkle hash-tree signatures, Merkle–Hellman knapsack encryption, Buchmann–Williams class-group encryption, ECDSA, HFEv-, and others (see Figure 1-12). They are going to fail with quantum algorithms and computing power. Shor’s algorithm is the quantum algorithm that threatens the RSA, DSA, and ECDSA cryptographic algorithms.

A quantum computer–based algorithm can break the symmetric key cryptographic algorithm by a factor of the square root of the size of the key. To find an image of a 256-bit hash function, a quantum algorithm will take only 2¹²⁸ time. Shor’s algorithm helps in the quantum factorization of big numbers. This demands that the security systems be quantum-resistant. Elliptic curve cryptography has been shown to improve the cracking process by 21 percent. This method is based on multiplying polynomials and adding random noise.

Quantum algorithms beat the classical equivalents because they can be executed on faster hardware. Quantum algorithms are also based on quantum mechanics. Theoretically, quantum mechanics methods have proven to be faster than classical methods. Quantum mechanics is related to the complex subatomic particle interaction. The subatomic particles can be electrons. Electrons can be present in multiple discrete states at the same time, which is called superposition .

Heisenberg’s uncertainty principle states that a quantum system has information about an object’s velocity and position. Any measurement of velocity will have an impact on the position because the act of observing modifies the state. The subatomic particles can be in an entangled state. A change to one particle impacts another particle when they are separated from each other. Quantum mechanics captures the complexities of the state of electrons using complex numbers.

Grover’s algorithm can solve a phone book search on the order of the square root of the number of phone book numbers (O(√n) time). Let’s say a phone book has 100 million phone numbers. Grover’s algorithm can find a number with 10,000 steps. Grover’s quantum algorithm uses quantum mechanics–based methods to speed up the search.

Optimization

Optimization problems occur in our daily life in different domains such as finance, banking, healthcare, logistics, and automotive sectors. In wealth management, portfolio selection is based on finding the best assets to balance the risk with the expected gains. A portfolio selection is an NP-hard problem. An NP-hard problem is a difficult and not impossible problem for current computers to solve quickly. Quantum annealing is a quantum algorithm to find solutions for these NP-hard problems.

In the annealing method , the problem is encoded to a similar physical problem. The lowest energy state of a system is the same as the solution to the problem at hand. This system is built in the annealer, and it evolves toward the lowest energy state. In real life, the annealer reaches the lowest energy through the cooling process. In quantum annealing, the tunneling events move the system to minima quickly. See Figure 1-13.

Quantum annealing techniques help in finding optimal trading paths. A large set of trading orders gets executed, and this impacts the asset price. The execution costs need to be accounted for during the trading execution. Algorithms need to set the trading rate for big orders. This helps in balancing the speed of trading, minimizing the exposure to market risk, and minimizing the impact on the market.

The other area is related to optimizing the arbitrage opportunities. Arbitrage is related to making a profit by taking advantage of the difference in asset prices with less risk. The example given is typically related to currency exchange from euros to dollars, dollars to yens, and yens to euros. This transaction might yield a small profit due to the differences in prices. This is called cross-currency arbitrage. This problem is NP-hard to find optimal arbitrage opportunities across the assets in different markets. The quantum annealer method helps in finding the path with the lowest cost.

Credit scoring is another area that can be solved using quantum annealing. Credit scoring of an individual is related to the person’s income, age, financial history, collateral, and current liabilities. The risk assessment looks at various factors in the customer’s profile. This is a computationally challenging problem, which is an NP-hard problem.

The vehicle routing problem is another optimization problem that can be solved using quantum annealing. Dantzig and Ramser proposed in 1959 the vehicle routing problem. The goal for the optimization is to identify the optimal routes for a set of vehicles serving customer orders. The solution is to identify the set of routes that are part of the order delivery from the source to the destination. The number of vehicles needed for customer orders is also an optimization goal. Quantum annealing is the optimization technique used to determine the local minima route over a given set of candidate functions. This is a method of discretizing a function with many local minima to determine the observables of the function. The quantum annealing process is different from simulated annealing, as it is based on the quantum tunneling process. The particles tunnel through energy barriers from a high state to a low state.

Quantum annealing starts from a superposition of different states of a system that are weighted equally. The time-dependent Schrödinger equation drives the time evolution of the system. This system serves to affect the amplitude of all states as the time increases. Eventually, the ground state is reached to give the instantaneous Hamiltonian of the system.

Cybersecurity

There are new applications in the post-quantum security area that operate on data at rest, in transit, in use, and in motion. These solutions work on data access, business processes, multiparty authentication, and multiparty authorization. These products are based on the algorithms and techniques to tackle man-in-middle detection, quantum security encryption, phishing resistance, biometric security, accountability, and segregation of duties. See Figure 1-14.

Data at rest can be intercepted and misused by an insider and an external hacker. Data in transit can be modified by the hackers for misuse in the networks. Confidential business data and personally identifiable information are the use cases for data in transit. Mission-critical data cannot be secured by using role-based access and controls. Data governance is needed in an enterprise to protect and secure the data.

Cybersecurity solutions are related to encrypt, decrypt, sign, and verify customer transactions and data. A hacker can intercept and steal the secured data. Secured data has information related to the customer’s credit card numbers and Social Security numbers. Using a quantum computer, the hacker has a higher computational power. Quantum algorithms like Shor’s are proven to break the current cryptographic systems. The post-quantum cryptographic designers are improving the efficiency of the new encryption and decryption algorithms. Hybrid systems and high-speed quantum-resistant algorithms are required to strengthen post-quantum cryptography algorithms. McEliece public-key encryption, NTRU public-key encryption, and lattice-based public key encryption systems are proven to be quantum-resistant.