Quantum framework for Reinforcement Learning: integrating Markov Decision Process, quantum arithmetic, and trajectory search

This paper introduces a quantum framework for addressing reinforcement learning (RL) tasks, grounded in the quantum principles and leveraging a fully quantum model of the classical Markov Decision Process (MDP). By employing quantum concepts and a quantum search algorithm, this work presents the implementation and optimization of the agent-environment interactions entirely within the quantum domain, eliminating reliance on classical computations. Key contributions include the quantum-based state transitions, return calculation, and trajectory search mechanism that utilize quantum principles to demonstrate the realization of RL processes through quantum phenomena. The implementation emphasizes the fundamental role of quantum superposition in enhancing computational efficiency for RL tasks. Experimental results demonstrate the capacity of a quantum model to achieve quantum advantage in RL, highlighting the potential of fully quantum implementations in decision-making tasks. This work not only underscores the applicability of quantum computing in machine learning but also contributes the field of quantum reinforcement learning (QRL) by offering a robust framework for understanding and exploiting quantum computing in RL systems.

DAISM: Digital Approximate In-SRAM Multiplier-based Accelerator for DNN Training and Inference

DNNs are one of the most widely used Deep Learning models. The matrix multiplication operations for DNNs incur significant computational costs and are bottlenecked by data movement between the memory and the processing elements. Many specialized accelerators have been proposed to optimize matrix multiplication operations. One popular idea is to use Processing-in-Memory where computations are performed by the memory storage element, thereby reducing the overhead of data movement between processor and memory. However, most PIM solutions rely either on novel memory technologies that have yet to mature or bit-serial computations which have significant performance overhead and scalability issues. In this work, an in-SRAM digital multiplier is proposed to take the best of both worlds, i.e. performing GEMM in memory but using only conventional SRAMs without the drawbacks of bit-serial computations. This allows the user to design systems with significant performance gains using existing technologies with little to no modifications. We first design a novel approximate bit-parallel multiplier that approximates multiplications with bitwise OR operations by leveraging multiple wordlines activation in the SRAM. We then propose DAISM - Digital Approximate In-SRAM Multiplier architecture, an accelerator for convolutional neural networks, based on our novel multiplier. This is followed by a comprehensive analysis of trade-offs in area, accuracy, and performance. We show that under similar design constraints, DAISM reduces energy consumption by 25\% and the number of cycles by 43\% compared to state-of-the-art baselines.

Quantum Circuit Fidelity Improvement with Long Short-Term Memory Networks

Quantum computing has entered the Noisy Intermediate-Scale Quantum (NISQ) era. Currently, the quantum processors we have are sensitive to environmental variables like radiation and temperature, thus producing noisy outputs. Although many proposed algorithms and applications exist for NISQ processors, we still face uncertainties when interpreting their noisy results. Specifically, how much confidence do we have in the quantum states we are picking as the output? This confidence is important since a NISQ computer will output a probability distribution of its qubit measurements, and it is sometimes hard to distinguish whether the distribution represents meaningful computation or just random noise. This paper presents a novel approach to attack this problem by framing quantum circuit fidelity prediction as a Time Series Forecasting problem, therefore making it possible to utilize the power of Long Short-Term Memory (LSTM) neural networks. A complete workflow to build the training circuit dataset and LSTM architecture is introduced, including an intuitive method of calculating the quantum circuit fidelity. The trained LSTM system, Q-fid, can predict the output fidelity of a quantum circuit running on a specific processor, without the need for any separate input of hardware calibration data or gate error rates. Evaluated on the QASMbench NISQ benchmark suite, Q-fid's prediction achieves an average RMSE of 0.0515, up to 24.7x more accurate than the default Qiskit transpile tool mapomatic. When used to find the high-fidelity circuit layouts from the available circuit transpilations, Q-fid predicts the fidelity for the top 10% layouts with an average RMSE of 0.0252, up to 32.8x more accurate than mapomatic.