ColdQuanta, a leader in cold atom quantum technology, and Classiq, which provides the leading software platform for Quantum Algorithm Design, are partnering to make 100-qubit quantum circuits. This will combine ColdQuanta’s cold atom quantum computer and Classiq’s quantum algorithm design software.

Nextbigfuture interviewed the Coldquanta CEO in March 2021.

Nextbigfuture interviewed Tom Noel, ColdQuanta’s Director of Quantum Computing and Interim CEO Dan Caruso. ColdQuanta is using neutral atoms instead of trapped ions as the basis for its quantum computers.

ColdQuanta is building a functional first system now and believes they will be able to scale to the 1000s of qubits faster than competing approaches. If usable quantum systems are created with high entanglement times and thousands of qubits then there will be massive advances with optimization problems, machine learning, materials research and chemistry simulation applications. Cracking major optimization problems could improve the efficiency of large-scale logistics at the scale of Fedex and the US military. Advances with chemistry will be beneficial for many industries including drugs and agriculture. The economic impacts would scale into the trillions.

This could enable the solution of real-world problems using quantum systems this year or next year.

Together, this combined solution provides customers the unique ability to create, simulate and execute unique quantum circuits to address a wide range of finance, material science, supply chain, and machine learning challenges.

Quantum computers solving complex problems requires complex quantum circuits. Computers with more qubits can balance more extensive financial portfolios, simulate more complex molecules and vaccines, analyze more difficult supply chain problems and tackle larger machine learning datasets. With this partnership, ColdQuanta and Classiq are unlocking a new set of possibilities in two important ways:

· Classiq’s quantum algorithm design platform makes it possible to create complex quantum circuits by starting from a high-level functional model of the circuit and then automatically synthesizing and optimizing a working quantum circuit from it.

· The ColdQuanta Hilbert quantum computer will offer companies and researchers the opportunity to simulate and execute 100-qubit quantum circuits, with even larger models becoming available in the future.

There are three major reasons the neutral atom approach should become dominant:

1. Ion qubits used in ion-trapping are positively charged. Both Honeywell and IonQ use what are called Paul traps, which use a radiofrequency electric field for trapping. Honeywell’s architecture uses physical motion to bring qubits into position to perform operations. IonQ’s architecture has fixed positions. The main point #1 about scaling that I was trying to make is that using neutral cold atoms as qubits simplifies using dense 2D arrays of qubits, compared to existing commercial ion trap architectures, which are limited to 1D chains. The dimensionality of the array makes faster scaling of qubit count possible.

The neutral atoms in an optical laser trap array can remain stationary. Electrons get activated to expand the range of quantum interactions. Neutral atoms will be trapped using a laser field and will be separated by about 2 to 10 microns of when held in the array.

Neutral atoms that do not have to be moved for quantum calculations will greatly simplify operations compared to ion-based approaches.

2. High Connections

DWave Systems, adiabitic quantum computing, has a high number of inferior superconducting qubits. DWave has created many versions of their chips and goes to great effort to increase the connections between different 16 qubit cells. The Google superconducting gate quantum computer has a connection level of about 4. Each qubit is connected to four others. The IBM superconducting gate computer has two to three connections per qubit. ColdQuanta will start with four connections and then scale up from there.

3. Entanglement

Trapped ion and neutral atoms approaches have far higher entanglement times. The superconducting approaches have severely limited entanglement times.

Lengthening entanglement times increases the numbers of operations and computing power for the systems. The entanglement metric helps to improve the depth of calculations possible with a quantum system. The number of qubits is the breadth while entanglement and speed of operations are major factors in the depth.

SOURCES- Coldquanta, March 2021 interview with Nextbigfuture.com

Written by Brian Wang, Nextbigfuture.com

Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.

Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.

A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.

I have a hard time seeing how these quantum computers will be useful for machine learning in the near term future.

If you want to optimize a ANN, for instance, you would have to keep all the weights in the qubit "memory", right? So an ANN with 1000 weights – absolutely tiny – would need at least a few thousand qubits.

To optimize newer networks with millions of weights you would then need a few million qubits, i.e. these quantum computer will only start to be useful when they are up to the size of at least millions of qubits.