Quantum circuit transformation aims to optimize circuits for depth, gate count, and compatibility with Noisy Intermediate Scale Quantum (NISQ) devices that suffer from various error sources. Prior methods use combinations of expert-defined rules and Reinforcement Learning (RL). We introduce AltGraph, a novel approach employing generative graph models to generate functionally equivalent quantum circuits using—specifically, Direct Acyclic Graph (DAG) Variational Autoencoder (D-VAE) variants (GRU and GCN) and Deep Generative Model for Graphs (DeepGMG). AltGraph perturbs the latent space to generate quantum circuits optimized for hardware coupling maps, reducing gate count by 37.55% and circuit depth by 37.75% post-transpiling, with 0.0074 Mean Squared Error (MSE) in the density matrix—outperforming state-of-the-art methods by 2.56%.

Quantum computing is a rapidly expanding field with applications ranging from optimization all the way to complex machine learning tasks. Quantum memories, while lacking in practical quantum computers, have the potential to bring quantum advantage. In quantum machine learning applications for example, a quantum memory can simplify the data loading process and potentially accelerate the learning task. Quantum memory can also store intermediate quantum state of qubits that can be reused for computation. However, the depth, gate count and compilation time of quantum memories such as, Quantum Read Only Memory (QROM) scale exponentially with the number of address lines making them impractical in state-of-the-art Noisy Intermediate-Scale Quantum (NISQ) computers beyond 4-bit addresses. In this paper, we propose techniques such as, pre-decoding logic and qubit reset to reduce the depth and gate count of QROM circuits to target wider address ranges such as, 8-bits. The proposed approach reduces the number of gates and depth count by at least 2X compared to the naive implementation at only 36% qubit overhead. A reduction in circuit depth and gate count as high as 75X and compilation time by 85X at the cost of a maximum of 2.28X qubit overhead is observed. Experimentally, the fidelity with the proposed pre-decoding circuit compared to existing optimization approach is also higher (as much as 73% compared to 40.8%) under reduced error rates.

Present-day quantum computers suffer from various noises or errors such as, gate error, relaxation, dephasing, readout error, and crosstalk. Besides, they offer a limited number of qubits with restrictive connectivity. Therefore, quantum programs running these computers face resilience issues and low output fidelities. The noise in the cloud-based access of quantum computers also introduce new modes of security and privacy issues. Furthermore, quantum computers face several threat models from insider and outsider adversaries including input tampering, program misallocation, fault injection, Reverse Engineering (RE) and Cloning. This paper provides an overview of various assets embedded in quantum computers and programs, vulnerabilities and attack models and the relation between resilience and security. We also cover countermeasures against the reliability and security issues and present future outlook for security of quantum computing.