The difference between programming a gate-based quantum computer and a quantum annealer

Ramin Ayanzadeh, Poulami Das, Swamit Tannu +1 more

Quantum computing is an information processing paradigm that uses quantum-mechanical properties to speedup computationally hard problems. Gate-based quantum computers and Quantum Annealers (QAs) are two commercially available hardware platforms that are accessible to users today. Although promising, existing gate-based quantum computers consist of only a few dozen qubits and are not large enough for most applications. On the other hand, existing QAs with few thousand qubits have the potential to solve some domain-specific optimization problems. QAs are single instruction machines and to execute a program, the problem is cast to a Hamiltonian, embedded in the hardware, and a single quantum machine instruction (QMI) is run. Unfortunately, noise and imperfections in hardware result in sub-optimal solutions on QAs even if the QMI is run for thousands of trials.Due to the limited programmability of QAs users execute the same QMI for all trials. This subjects all trials to a similar noise profile throughout the execution, resulting in a systematic bias. We observe that systematic bias leads to sub-optimal solutions and cannot be alleviated by executing more trials or using existing error-mitigation schemes. To address this challenge, we propose EQUAL (Ensemble QUantum AnneaLing). EQUAL generates an ensemble of QMIs by adding controlled perturbations to the program QMI. When executed on the QA, the ensemble of QMIs steers the program away from encountering the same bias during all trials and thus, improves the quality of solutions. Our evaluations using the 2041-qubit D-Wave QA show that EQUAL bridges the difference between the baseline and the ideal by an average of 14% (and up to 26%) without requiring any additional trials. EQUAL can be combined with existing error mitigation schemes to further bridge the difference between the baseline and ideal by an average of 55% (and up to 68%).

Kristel F. L. Michielsen

Ramin Ayanzadeh, Poulami Das, Swamit Tannu +1 more

Quantum computing is an information processing paradigm that uses quantum-mechanical properties to speedup computationally hard problems. Although promising, existing gate-based quantum computers consist of only a few dozen qubits and are not large enough for most applications. On the other hand, existing QAs with few thousand of qubits have the potential to solve some domain-specific optimization problems. QAs are single instruction machines and to execute a program, the problem is cast to a Hamiltonian, embedded on the hardware, and a single quantum machine instruction (QMI) is run. Unfortunately, noise and imperfections in hardware result in sub-optimal solutions on QAs even if the QMI is run for thousands of trials. The limited programmability of QAs mean that the user executes the same QMI for all trials. This subjects all trials to a similar noise profile throughout the execution, resulting in a systematic bias. We observe that systematic bias leads to sub-optimal solutions and cannot be alleviated by executing more trials or using existing error-mitigation schemes. To address this challenge, we propose EQUAL (Ensemble Quantum Annealing). EQUAL generates an ensemble of QMIs by adding controlled perturbations to the program QMI. When executed on the QA, the ensemble of QMIs steers the program away from encountering the same bias during all trials and thus, improves the quality of solutions. Our evaluations using the 2041-qubit D-Wave QA show that EQUAL bridges the difference between the baseline and the ideal by an average of 14% (and up to 26%), without requiring any additional trials. EQUAL can be combined with existing error mitigation schemes to further bridge the difference between the baseline and ideal by an average of 55% (and up to 68%).

Rafael Pereira da Silva

Rudolf Marcel Füchslin

Eneko Osaba, Pablo Miranda-Rodriguez, Andreas Oikonomakis +4 more

This paper presents a novel hybrid approach to solving real-world drone routing problems by leveraging the capabilities of quantum computing. The proposed method, coined Quantum for Drone Routing (Q4DR), integrates the two most prominent paradigms in the field: quantum gate-based computing, through the Eclipse Qrisp programming language; and quantum annealers, by means of D-Wave System’s devices. The algorithm is divided into two different phases: an initial clustering phase executed using a Quantum Approximate Optimization Algorithm (QAOA), and a routing phase employing quantum annealers. The efficacy of Q4DR is demonstrated through three use cases of increasing complexity, each incorporating real-world constraints such as asymmetric costs, forbidden paths, and itinerant charging points. This research contributes to the growing body of work in quantum optimization, showcasing the practical applications of quantum computing in logistics and route planning.