Who would have thought that last week, the USA quantum computing company, which didn't perform well against Google's Willow chip, $Quantum Computing (QUBT.US)$ , experienced a consecutive surge this week.

As of the close of the USA stock market, the quantum computing company $Quantum Computing (QUBT.US)$ has risen over 50%, continuing to hit a historical high, with the previous trading day seeing an increase of 65%. As of this report, it has risen another 13% in after-hours trading.

As for the reason for the surge on Tuesday, the quantum computing company announced that it secured a contract with NASA's Goddard Space Flight Center and will use the company's entropy quantum computer Dirac-3 to address "advanced imaging and data processing needs."

Specifically, the project aims to utilize Dirac-3 to solve phase unfolding issues, where the company will help NASA optimally reconstruct images and extract information from interference data generated by Radar. During this period, the quantum computing company will assist NASA in performing interference imaging at all scales, thereby enhancing data quality and accuracy.

In simple terms, NASA captures a large number of blurry photos via Satellite — because a lot of data in the images has been folded, which causes some parts to appear misaligned. Therefore, NASA sought help from a quantum computing company to 'unfold' these misaligned parts and restore the true terrain or image, which is known as phase unwrapping.

The company believes that this project will demonstrate the advantages of quantum computers in handling 'NP-hard' problems, and enable NASA to compare quantum optimization techniques with algorithms on classical computers. To explain this in simple terms, 'NP-hard' problems refer to those for which we do not know how to find the optimal solution in a short time, but once an answer is found, it is easy to verify, often requiring a huge amount of computation.

'NP-hard' problem example: The Knapsack Problem:

Suppose you have a bag with a weight limit of 10 kilograms, and a pile of items in front of you, each with its own weight and value. The task is to select some items to put into the bag while not exceeding the total weight limit, making the total value of the items as high as possible.

In this problem, assuming there are 100 items in front of you, there will be 2^100 possible combinations, needing to check all the solutions to find the optimal one! In reality, the number of 'items' could reach 200, 500, 1000, or even more!

Note! Different from Google's Willow.

Compared to Google's Willow quantum chip, which cannot even guarantee 'correct computation', Dirac-3 is a commercially available quantum optimization machine. The company offers cloud access at a cost of $1000 per hour or a direct purchase of a machine for $0.3 million. The machine can be installed directly in standard racks, runs at room temperature, and consumes no more than 100W.

(This is what the machine looks like, source: company official website)

From the perspective of problem-solving principles, Dirac-3 and Willow follow different paths.

Dirac-3 is based on quantum optics and photonics technology, using the parallelism and interference of light waves to find the optimal solution to a problem, essentially functioning as a quantum optimizer. Compared to ordinary computers, which can only test results one by one, Dirac-3 can test all possible answers simultaneously, and then the 'light of the correct answer' becomes brighter due to constructive interference, making it immediately apparent.

The quantum computing company claims that Dirac-3 can handle up to 949 variables and is suitable for solving discrete optimization problems that require extensive search and computation.

Willow, on the other hand, is based on superconducting qubit quantum computing chips, utilizing principles of quantum mechanics such as quantum superposition and quantum entanglement to perform computations, focusing on the universality of quantum computing.

Editor/Rocky