Research Roundup for September 2023

Kudos to: Dr Chris Mansell

Hardware

Title: Fast, High-Fidelity Addressed Single-Qubit Gates Using Efficient Composite Pulse Sequences Organizations: University of Oxford The?number of logic gates that can be implemented in a quantum computer is limited either by the gate duration (compared to the decoherence time) or by the fidelity (compared to the acceptable precision in the output). Single-qubit gates on trapped-ion qubits already have some of the highest fidelities achieved across all varieties of hardware platforms. In the experiment presented in this paper, microwave near-field radiation was used to perform a single-qubit operation on an ion approximately 20 times faster while still maintaining a state-of-the-art fidelity. The demonstrated technique is much less experimentally challenging than more conventional schemes where the ions are optically addressed. Link: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.131.120601

Title: Anomaly detection speed-up by quantum restricted Boltzmann machines Organizations: Politecnico di Milano; Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche; Università degli Studi di Milano Adiabatic quantum annealers presently have more qubits than circuit-model quantum computers and can therefore address larger problems. They offer a new way to train and query restricted Boltzmann machines (RBMs). In this paper, the performance, training times and query times of an RBM implemented on a D-wave annealer are compared with those of a classical RBM. The authors found that the queries could be up to 64 times faster. This speed-up depends on the characteristics of the problem at hand. In this case, it was a classification problem involving a real-world cybersecurity dataset. Link: https://www.nature.com/articles/s42005-023-01390-y

Title: A quantum engine in the BEC–BCS crossover Organizations: OPTIMAS; OIST Graduate University; National Scientific and Technical Research Council of Argentina; National University of Córdoba; University of Stuttgart Ultracold atoms can all occupy the ground state at the same time if they are bosons. However, if they are fermions, they have to occupy different energy levels. Importantly, fermions can pair up to form composite particles that behave like bosons. In this paper, a magnetic field was tuned back and forth across a Feshbach resonance in order to control the pairing of fermionic lithium atoms. Along with varying the potential in which the atoms were trapped, this control allowed a thermodynamic cycle to be implemented. While it is reminiscent of a traditional engine, its operation directly depended on the quantum statistics of the atoms – that is, whether they were bosonic or fermionic. Due to this astounding design, heat baths were not needed.? Link: https://www.nature.com/articles/s41586-023-06469-8

Title: Leveraging Analog Quantum Computing with Neutral Atoms for Solvent Configuration Prediction in Drug Discovery Organizations: Pasqal; Qubit Pharmaceuticals; Sorbonne Université This research introduces two analog quantum algorithms to efficiently sample the probability distribution of water molecules’ positions within proteins, addressing a critical challenge in drug design and molecular modelling. The first algorithm employs quantum adiabatic evolution cast into an NP-hard Ising model and implemented on a Rydberg atom QPU. The second, a variational quantum approach, optimises the parameters of the lasers controlling the atoms. Due to limitations in the hardware, this algorithm was only classically emulated. Future work will explore both of these algorithms on next-generation arrays of neutral atoms that, while larger, should have a similar level of noise. Link: https://arxiv.org/abs/2309.12129

Software

Title: Error mitigation for quantum approximate optimization Organizations: University of Innsbruck, Parity Quantum Computing GmbH In parity quantum computing, pairs of qubits in the computational basis states are used to represent zeroes and ones: if both qubits of the pair are in the same state as each other, they represent a zero; if they are in different states, they represent a one. First conceived in 2015, this encoding paradigm offers several features that allow it to overcome the challenges of the NISQ era. In this new work, a novel, parity-based error mitigation technique is applied to the quantum approximate optimization algorithm. By exploiting redundant information, it obtains a better success probability than a standard method. Link: https://journals.aps.org/pra/abstract/10.1103/PhysRevA.108.032408

Title: Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits Organizations: Harvard University; NVIDIA; California Institute of Technology Can distributed quantum computation be performed in the NISQ era? Perhaps it can if the computation is approximate and only limited amounts of entanglement and information transfer are required.?In this work, the researchers investigated the performance of Hamiltonian simulation and VQE when they transferred classical information about mean-field corrections between quantum processing units. They considered the effect of also communicating quantum information about auxiliary qubits by either qubit shuffling or quantum teleportation. For VQE, implementing the step known as pre-training or warm-starting while the qubits were partitioned into different subgroups reduced errors by several orders of magnitude while requiring about ten times fewer iterations. Link:?https://arxiv.org/abs/2309.05693

Title: A Modular Engine for Quantum Monte Carlo Integration Organizations: Quantinuum; University of the Witwatersrand; University of Cambridge In mathematical finance, an option is an asset with a payoff that can depend on its price not only at its expiry time but also at some number of earlier times. Calculating the expected payoff involves integrating a high-dimensional multivariate probability distribution, where each of the earlier times is one of the dimensions of the distribution. Monte Carlo integration is widely employed in the financial sector for this task. This white paper presents a quantum Monte Carlo integration engine that uses quantum circuits to encode models of financial time-series and includes a statistically robust quantum amplitude estimation algorithm.? Link: https://arxiv.org/abs/2308.06081

Title: Quantum-Informed Recursive Optimization Algorithms Organizations: BMW; Technical University Munich; Amazon The quantum approximate optimization algorithm can be performed in a recursive manner where each step eliminates one variable from the problem until it is small enough to solve. Until now, every type of optimiszation problem in this formulation was treated the same way. In this preprint, the update steps now depend on whether the problem is, say, a maximum independent set problem or a maximum satisfiability problem. This allows opportunities for classical subroutines to improve the algorithm’s performance, for hard constraints to be imposed, and for backtracking to be employed without needing deeper quantum circuits. Link: https://arxiv.org/abs/2308.13607

Title: Non-Linear Transformations of Quantum Amplitudes: Exponential Improvement, Generalization, and Applications Organizations: University of Oxford; JPMorgan Chase; National University of Singapore Nonlinear functions are ubiquitous in applied mathematics. Quantum computers can implement many of these nonlinear transformations but to do so, they must work in the subspaces of higher dimensional unitaries. Developing a better understanding of how to operate in these subspaces is therefore a compelling research topic. Until this preprint, nonlinear transformations were always applied to uniform superpositions. Here, the authors consider other quantum states, as defined by state preparation unitaries, and find that this more general approach leads to considerable improvements. They showcase the versatility of their ideas by applying them to topics such as quantum machine learning.? Link: https://arxiv.org/abs/2309.09839

Title: Quantum Gauge Networks: A New Kind of Tensor Network Organizations: Rice University; California Institute of Technology Simulating strongly correlated quantum systems is a challenging task and while tensor network algorithms do a good job when the system has one spatial dimension, they are much less computationally efficient for 2D and 3D systems. In this paper, a new kind of tensor network, named a quantum gauge network, attempts to improve this situation. Local wave functions are truncated and used in approximate simulations. This leads to at least six advantages over existing tensor network methods, including a very favourable runtime scaling. Being such a new type of network, there is considerable room for algorithmic improvements as well as interesting connections to explore.? Link: https://quantum-journal.org/papers/q-2023-09-14-1113/