Scalable Quantum Computing with Superconducting Qubits

Walter Riess IBM Research – Zurich [email protected] The Future of Computing – An industry perspective Next Generation Systems 1958 1971 2014 3D / hybrid

First integrated circuit Moore’s Law is Born IBM P8 Processor ~ 650 mm2 Size ~1cm2 Intel 4004 22 nm feature size, 16 cores Cognitive (neuromorphic) 2 Transistors 2,300 transistors > 4.2 Billion Transistors Computing

Quantum Computing

“hard” / intractable problems: (exponentially increasing resources with problem size)

• Algebraic algorithms (e.g. factoring, systems of equations) for machine learning, cryptography,… • Combinatorial optimization (traveling salesman, optimizing business processes) Hard Problems for • Simulating quantum mechanics (chemistry, material science,…) Classical Computing

Easy Problems Possible with 13 x 7 = ? Quantum Computing 937 x 947 = ? 91 = ? x ? 887339 = ? x ?

Superconducting Qubit: . non-linear Josephson Junction (Inductance) . anharmonic energy spectrum => qubit

. nearly dissipationless => T1, T2 ~ 70 µs

Microwave Resonator as: . read-out of qubit states . multi-qubit quantum bus . noise filter The Superconducting Quantum Computing Setup

2.7K -270℃

Chip with superconducting 0.8K qubits and resonators

0.1K

Microwave electronics 0.02K PCB with the qubit chip at 20mK |1〉 Protected+ from the environment by multiple shields |0〉

4 Qubits (2015) 5 Qubits (2016)

Latticed arrangement for scaling

05/2016: 5 Qubits hosted on IBM Quantum Experience © 05/2017:2017 International 16 Qubits Business on Machines Quantum Corporation Experience, 17 Qubits on IBMQ (commercial) How powerful is “my” Quantum Computer

Quantum Volume

Number of qubits (more is better)

Errors (less is better)

Connectivity (more is better)

Gates set (more is better)

The quantum volume measures the useful amount of quantum computing done by a device in space and time.

Solving interacting fermionic problems is at the core of most challenges in computational physics and high-performance computing: 푁 푁 푀 1 2 푍퐴 1 퐻푒 = − 훻푖 − + 2 푟푖퐴 푟푖푗 푖=1 푖=1 퐴=1 푗>1 What can quantum computers do? Map fermions (electrons) to qubits and compute molecular structure reaction rates reaction pathways

Sign problem: Monte-Carlo simulations of fermions are NP-hard [Troyer &Wiese, PRL 170201 (2015)] © 2017 International Business Machines Corporation Roadmap: Quantum Systems complement classical Systems

A small quantum computer is combined with a classical computer to jointly solve a computational task.

A simple hybrid quantum-classical algorithm can be used to solve problems where the goal is to minimize the energy of a system.

Prepare a trial state 휓 휃 and compute its energy 퐸(휃) Use classical optimizer to choose a new value of 휃 to try Advantages: Use short circuits which fit into our coherence time Improve on best classical estimates by using non-classical trial states © 2017 International Business Machines Corporation Groundstate-energy of simple molecules

Using six qubits of a seven-qubit processor it was able to measure BeH2’s lowest energy state, a key measurement for understanding chemical reactions. While this model of

BeH2 can be simulated on a classical computer, IBM’s approach has the potential to scale towards investigating larger molecules that would traditionally be seen to be beyond the scope of classical computational methods, as more powerful quantum systems get built.

퐇ퟐ: 2 qubits LiH: 4 qubits 퐁퐞퐇ퟐ: 6 qubits 5 pauli terms, 2 sets 100 pauli terms, 25 sets 144 pauli terms, 36 sets

Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets Abhinav Kandala1*, Antonio Mezzacapo1*, Kristan Temme1, Maika Takita1, Markus Brink1, Jerry M. Chow1 & Jay M. Gambetta1, doi:10.1038/nature23879 © 2017 International Business Machines Corporation Grand Challenge: Quantum Computing

Goal: Build computers based on quantum physics to solve problems that are otherwise intractable

Roadmap: Small-scale (Quantum advantage) Medium-scale (approximate QC) Large-scale (Universal QC) . Research level demonstrations . Develop “Hardware-efficient” apps Known and proven speed-up: . Verify chemistry and error correction − Chemical configurations . Factoring principles − Simple Optimization . quantum molecular simulations . Infrastructure & community building − Hybrid quantum-classical computers Machine learning, optimization . Demonstrate ‘Quantum advantage’ . No full error correction available Enable secure cloud computing

5-8 qubits 16-20 qubits 50-100+ qubits 105-106 qubits

Challenges: Continued scalability, control and coherence of large systems,…

Fabrication/3D Integration

Superconducting Quantum Processor . can be engineered . builds on existing technologies . challenges in coherence, control complexity and scaling Microwave circuit design Control Software & Quantization System Characterization Quantum Algorithms Ways you can engage IBM with Quantum

Public usage of the IBM Q IBM Research Early access to IBM Q System experience Frontiers Institute https://www.ibm.com/research/ https://www.research.ibm.com/ibm-q/ frontiers https://www.research.ibm.com/ibm-q/

November 7, 2017

IBM Quantum Computing European Workshop A one-day event held at IBM Research – Zurich

Independent experimentation Partner to Develop World’s most advanced and learning Quantum Applications hardware

Factoring and therefore encryption breaking using current schemes will not be a significant application of quantum computing for many years. By the time we can do this, we will have changed our encryption algorithms (PQC).

But this doesn’t mean one should do nothing! If you have data which needs to be safe decades from now you can already begin to make it quantum safe…… Thank you for your attention!

Walter Riess [email protected] IBM Research - Zurich